Chapter 5 Community composition

load("data/data.Rdata")

5.1 Taxonomy overview

5.1.1 Stacked barplot

# Merge data frames based on sample
transplants_metadata<-sample_metadata%>%
  mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)

merged_data<-genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., transplants_metadata, by = join_by(sample == Tube_code)) %>% #append sample metadata
  filter(count > 0) #filter 0 counts

ggplot(merged_data, aes(x=newID,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors) +
    facet_nested(. ~ time_point + type ,  scales="free") + #facet per day and treatment
    guides(fill = guide_legend(ncol = 1)) +
    labs(fill="Phylum",y = "Relative abundance",x="Sample")+
    theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size=0))

5.1.2 Phylum relative abundances

phylum_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count))

phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun, na.rm=TRUE),sd=sd(relabun, na.rm=TRUE)) %>%
    arrange(-mean) %>%
    tt()

phylum	mean	sd
p__Bacteroidota	0.387533390	0.199408886
p__Bacillota_A	0.247736092	0.157285090
p__Bacillota	0.119551625	0.147933278
p__Pseudomonadota	0.094124112	0.158574923
p__Campylobacterota	0.054878320	0.094197988
p__Verrucomicrobiota	0.027807574	0.066974042
p__Desulfobacterota	0.023737705	0.036771812
p__Chlamydiota	0.010961883	0.060750276
p__Fusobacteriota	0.010557257	0.028638395
p__Cyanobacteriota	0.009276509	0.016635288
p__Bacillota_C	0.004811016	0.006724154
p__Spirochaetota	0.004098862	0.012506256
p__Bacillota_B	0.002556218	0.004994779
p__Actinomycetota	0.001281229	0.006458780
p__Elusimicrobiota	0.001088209	0.006278019

phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    filter(phylum %in% phylum_arrange) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
        geom_jitter(alpha=0.5) + 
        theme_minimal() + 
        theme(legend.position="none") +
        labs(y="Phylum",x="Relative abundance")

5.2 Taxonomy boxplot

5.2.1 Family

family_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>% #append sample metadata
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,family) %>%
  summarise(relabun=sum(count))

family_summary %>%
    group_by(family) %>%
    summarise(mean=mean(relabun, na.rm=TRUE),sd=sd(relabun, na.rm=TRUE)) %>%
    arrange(-mean) %>%
    tt()

family	mean	sd
f__Bacteroidaceae	2.260146e-01	0.1384706235
f__Lachnospiraceae	1.410833e-01	0.1062953893
f__Tannerellaceae	1.045659e-01	0.0799894745
f__Helicobacteraceae	5.448546e-02	0.0937279764
f__Mycoplasmoidaceae	3.756572e-02	0.0767893776
f__Erysipelotrichaceae	3.536287e-02	0.0452140267
f__UBA3700	3.456595e-02	0.0565495010
f__Marinifilaceae	2.794365e-02	0.0272474083
f__Rikenellaceae	2.725202e-02	0.0471735191
f__Enterobacteriaceae	2.687327e-02	0.0929254305
f__Coprobacillaceae	2.627823e-02	0.0907456387
f__	2.465083e-02	0.0781013121
f__Desulfovibrionaceae	2.373771e-02	0.0367718116
f__DTU072	2.183007e-02	0.0377159876
f__Ruminococcaceae	1.832093e-02	0.0428115194
f__Rhizobiaceae	1.579679e-02	0.0779688169
f__LL51	1.556592e-02	0.0616955422
f__UBA3830	1.512118e-02	0.0441855701
f__Akkermansiaceae	1.224165e-02	0.0317653885
f__Chlamydiaceae	1.096188e-02	0.0607502761
f__Fusobacteriaceae	1.055726e-02	0.0286383947
f__CAG-239	9.138683e-03	0.0152490206
f__Enterococcaceae	8.437601e-03	0.0473906561
f__Gastranaerophilaceae	7.848357e-03	0.0146292952
f__Oscillospiraceae	6.624721e-03	0.0075288565
f__UBA1997	6.613196e-03	0.0315103296
f__Streptococcaceae	6.600789e-03	0.0348230465
f__UBA1242	4.266475e-03	0.0147768596
f__Brevinemataceae	4.098862e-03	0.0125062564
f__Acutalibacteraceae	3.498766e-03	0.0111374416
f__RUG11792	2.921450e-03	0.0255676374
f__Clostridiaceae	2.855351e-03	0.0174153876
f__UBA660	2.600647e-03	0.0118148140
f__Peptococcaceae	2.556218e-03	0.0049947786
f__Acidaminococcaceae	1.980431e-03	0.0051045211
f__CAG-508	1.874529e-03	0.0065256902
f__MGBC116941	1.783700e-03	0.0077801927
f__Moraxellaceae	1.540093e-03	0.0099192011
f__RUG14156	1.428152e-03	0.0045670616
f__Staphylococcaceae	1.411833e-03	0.0051727635
f__Anaerovoracaceae	1.410739e-03	0.0027876311
f__Elusimicrobiaceae	1.088209e-03	0.0062780187
f__CAG-288	9.840222e-04	0.0061275213
f__Anaerotignaceae	9.320656e-04	0.0041174457
f__CALVMC01	7.793540e-04	0.0044385554
f__Eggerthellaceae	6.643755e-04	0.0021620275
f__Massilibacillaceae	6.322621e-04	0.0016561037
f__Mycobacteriaceae	6.168531e-04	0.0061497354
f__UBA1820	4.705627e-04	0.0013078764
f__CAG-274	4.686117e-04	0.0022415212
f__Arcobacteraceae	3.928587e-04	0.0050156837
f__Burkholderiaceae_C	3.835606e-04	0.0048969735
f__Muribaculaceae	3.508548e-04	0.0009525792
f__UBA932	3.295199e-04	0.0011408058
f__Hepatoplasmataceae	3.099135e-04	0.0039567109
f__Rhodobacteraceae	3.068016e-04	0.0039169801
f__Weeksellaceae	2.873650e-04	0.0032049404
f__Eubacteriaceae	1.707442e-04	0.0006844943
f__Sphingobacteriaceae	1.561202e-04	0.0012685229
f__Devosiaceae	1.544841e-04	0.0015368528
f__Pumilibacteraceae	1.324439e-04	0.0007783049
f__WRAU01	9.956857e-05	0.0012712064
f__Peptostreptococcaceae	2.371535e-05	0.0003027773

family_arrange <- family_summary %>%
    group_by(family) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(family) %>%
    pull()

family_summary %>%
    left_join(genome_metadata %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
    left_join(sample_metadata,by=join_by(sample==Tube_code)) %>%
    filter(family %in% family_arrange[1:20]) %>%
    mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=family, group=family, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~type)+
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")

5.2.2 Genus

genus_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>% #append sample metadata
  left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,genus) %>%
  summarise(relabun=sum(count)) %>%
  filter(genus != "g__")

genus_summary %>%
    group_by(genus) %>%
    summarise(mean=mean(relabun, na.rm=TRUE),sd=sd(relabun, na.rm=TRUE)) %>%
    arrange(-mean) %>%
    tt()

genus	mean	sd
g__Bacteroides	1.374441e-01	0.0923562611
g__Parabacteroides	9.843371e-02	0.0803813676
g__Phocaeicola	7.109518e-02	0.0799972428
g__Helicobacter_J	3.115738e-02	0.0603033642
g__Mycoplasmoides	3.115302e-02	0.0765633496
g__Odoribacter	2.605667e-02	0.0268723184
g__Roseburia	2.425344e-02	0.0567044550
g__NHYM01	2.332808e-02	0.0810376677
g__Alistipes	2.224698e-02	0.0287419149
g__Coprobacillus	2.070109e-02	0.0894282233
g__Agrobacterium	1.579679e-02	0.0779688169
g__Akkermansia	1.224165e-02	0.0317653885
g__Fusobacterium_A	1.046073e-02	0.0286441117
g__Kineothrix	9.107908e-03	0.0416218616
g__Proteus	8.976683e-03	0.0694135711
g__Dielma	8.687357e-03	0.0090713197
g__CAG-95	8.238073e-03	0.0207930753
g__JAAYNV01	7.265789e-03	0.0179169564
g__Desulfovibrio	7.219276e-03	0.0214990147
g__UBA866	7.016767e-03	0.0295145125
g__Enterococcus	6.966137e-03	0.0463712943
g__Lactococcus	6.600789e-03	0.0348230465
g__Ureaplasma	6.412700e-03	0.0139267552
g__Parabacteroides_B	6.132159e-03	0.0101543965
g__Lacrimispora	6.028411e-03	0.0098068179
g__CALXRO01	5.977964e-03	0.0313982647
g__Citrobacter	5.896711e-03	0.0340533686
g__NSJ-61	5.745781e-03	0.0202234473
g__Breznakia	5.530147e-03	0.0240721461
g__Clostridium_AQ	5.522246e-03	0.0123487838
g__Bilophila	5.044501e-03	0.0089558435
g__Hungatella_A	4.964136e-03	0.0096921078
g__Escherichia	4.342538e-03	0.0270859242
g__Salmonella	4.319018e-03	0.0148769561
g__UMGS1251	4.312965e-03	0.0073601071
g__MGBC136627	4.305492e-03	0.0164533523
g__Hungatella	4.150386e-03	0.0194068227
g__Clostridium_Q	4.146767e-03	0.0052575243
g__Brevinema	4.098862e-03	0.0125062564
g__Thomasclavelia	4.046233e-03	0.0110779301
g__Scatousia	3.752075e-03	0.0104403539
g__Mailhella	3.745039e-03	0.0104110785
g__Copromonas	3.643508e-03	0.0050495456
g__Enterocloster	3.613702e-03	0.0047492729
g__Ventrimonas	3.566172e-03	0.0071931788
g__Fournierella	3.313097e-03	0.0063192740
g__Limenecus	3.230504e-03	0.0066725343
g__Mucinivorans	3.006847e-03	0.0379999623
g__Lawsonia	2.916613e-03	0.0103686789
g__MGBC133411	2.902785e-03	0.0074333461
g__Caccovivens	2.887473e-03	0.0112659902
g__Sarcina	2.855351e-03	0.0174153876
g__Eisenbergiella	2.796704e-03	0.0069384489
g__Bacteroides_G	2.781473e-03	0.0352463088
g__CAJLXD01	2.730769e-03	0.0088951735
g__Acetatifactor	2.654208e-03	0.0055286194
g__Blautia	2.598789e-03	0.0062369300
g__Velocimicrobium	2.235984e-03	0.0067748392
g__C-19	2.235603e-03	0.0048296119
g__CAZU01	2.189719e-03	0.0066369837
g__Negativibacillus	2.145239e-03	0.0056002700
g__Intestinimonas	2.003816e-03	0.0035552824
g__Rikenella	1.998193e-03	0.0037323264
g__Phascolarctobacterium	1.980431e-03	0.0051045211
g__Butyricimonas	1.886974e-03	0.0042483569
g__RGIG6463	1.855727e-03	0.0040258495
g__MGBC116941	1.783700e-03	0.0077801927
g__JALFVM01	1.712574e-03	0.0038669765
g__Oscillibacter	1.546231e-03	0.0025273862
g__Acinetobacter	1.540093e-03	0.0099192011
g__Pseudoflavonifractor	1.489286e-03	0.0027026675
g__Citrobacter_A	1.444197e-03	0.0061395639
g__Staphylococcus	1.411833e-03	0.0051727635
g__14-2	1.228249e-03	0.0098038984
g__RGIG4733	1.225656e-03	0.0038271024
g__Beduini	1.217163e-03	0.0025500013
g__Scatocola	1.162463e-03	0.0045748144
g__Enterococcus_A	1.123893e-03	0.0100947603
g__UBA1436	1.088209e-03	0.0062780187
g__Faecisoma	1.057958e-03	0.0056043491
g__RGIG9287	9.993630e-04	0.0094920364
g__CAG-345	9.840222e-04	0.0061275213
g__Lachnotalea	9.593025e-04	0.0033990676
g__Blautia_A	9.546974e-04	0.0029560949
g__Ruthenibacterium	8.602962e-04	0.0024818365
g__CAG-269	8.276280e-04	0.0048017072
g__Marseille-P3106	8.230475e-04	0.0017874580
g__WRHT01	6.666234e-04	0.0027445999
g__Eggerthella	6.643755e-04	0.0021620275
g__CHH4-2	6.371240e-04	0.0020328940
g__Corynebacterium	6.168531e-04	0.0061497354
g__Serratia_A	6.076344e-04	0.0077577570
g__Anaerotruncus	6.058602e-04	0.0016447558
g__RUG14156	5.735678e-04	0.0021869659
g__RGIG1896	5.683407e-04	0.0051791669
g__IOR16	5.574841e-04	0.0016418264
g__Faecimonas	5.146607e-04	0.0054508437
g__CAG-56	5.096368e-04	0.0016613952
g__MGBC140009	4.851579e-04	0.0024491799
g__CALURL01	4.805911e-04	0.0017020401
g__Merdimorpha	4.705627e-04	0.0013078764
g__RGIG8482	4.560993e-04	0.0030287706
g__Enterobacter	4.223379e-04	0.0042068345
g__Klebsiella	4.203682e-04	0.0049802041
g__Caccenecus	4.086273e-04	0.0018112589
g__Aliarcobacter	3.928587e-04	0.0050156837
g__Scatenecus	3.851876e-04	0.0018282510
g__Alcaligenes	3.835606e-04	0.0048969735
g__Plesiomonas	3.766988e-04	0.0027593254
g__JAHHSE01	3.529590e-04	0.0014998851
g__HGM05232	3.508548e-04	0.0009525792
g__Enterococcus_B	3.475714e-04	0.0022665993
g__Egerieousia	3.295199e-04	0.0011408058
g__Stoquefichus	3.137462e-04	0.0020871798
g__Hepatoplasma	3.099135e-04	0.0039567109
g__Paracoccus	3.068016e-04	0.0039169801
g__Moheibacter	2.873650e-04	0.0032049404
g__Scatomorpha	2.738230e-04	0.0010358302
g__Emergencia	2.601331e-04	0.0013298673
g__UBA7185	2.523935e-04	0.0014817660
g__Eubacterium	1.707442e-04	0.0006844943
g__Sphingobacterium	1.561202e-04	0.0012685229
g__Devosia	1.544841e-04	0.0015368528
g__Anaerosporobacter	1.507638e-04	0.0012978048
g__Caccomorpha	1.434035e-04	0.0010730603
g__UBA2658	1.355578e-04	0.0007332702
g__Protoclostridium	1.324439e-04	0.0007783049
g__Angelakisella	1.315171e-04	0.0009387427
g__Cetobacterium_A	9.652924e-05	0.0008876688
g__Rahnella	6.708891e-05	0.0008565338
g__Peptostreptococcus	2.371535e-05	0.0003027773

genus_arrange <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=sum(relabun)) %>%
    filter(genus != "g__")%>%
    arrange(-mean) %>%
    select(genus) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    pull()

genus_summary %>%
    left_join(genome_metadata %>% select(genus,phylum) %>% unique(),by=join_by(genus==genus)) %>%
    left_join(sample_metadata,by=join_by(sample==Tube_code)) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    filter(genus %in% genus_arrange[1:20]) %>%
    mutate(genus=factor(genus,levels=rev(genus_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
        scale_color_manual(values=phylum_colors[-c(3,4,6,8)]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~type)+
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")